Trustworthy AI: Validity, Fairness, Explainability, and Uncertainty Assessments

This lesson plan aims to introduce machine learning practitioners about responsible machine learning, with a focus on fairness, explanability, and reproducibility concerns. This lesson is developed through The Carpentries Workbench framework and is designed to be taught as a Carpentries workshop.

1. Adjust the CODE_OF_CONDUCT.md, CONTRIBUTING.md, and LICENSE.md files as appropriate for your project.
   • CODE_OF_CONDUCT.md: if you are using this template for a project outside The Carpentries, you should adjust this file to describe who should be contacted with Code of Conduct reports, and how those reports will be handled.
   • CONTRIBUTING.md: depending on the current state and maturity of your project, the contents of the template Contributing Guide may not be appropriate. You should adjust the file to help guide contributors on how best to get involved and make an impact on your lesson.
   • LICENSE.md: in line with the terms of the CC-BY license, you should ensure that the copyright information provided in the license file is accurate for your project.
2. Update this README with relevant information about your lesson and delete this section.